Feature Extraction
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
visual-document-retrieval
multi-vector
late-interaction
colbert
index-compression
hierarchical-pooling
text-to-image
Instructions to use hltcoe/ColBERT_qwen2.5-vl_colpali with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hltcoe/ColBERT_qwen2.5-vl_colpali with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hltcoe/ColBERT_qwen2.5-vl_colpali")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hltcoe/ColBERT_qwen2.5-vl_colpali") model = AutoModelForMultimodalLM.from_pretrained("hltcoe/ColBERT_qwen2.5-vl_colpali", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bd2f092eeec244c0448f13e31edd4b25e625568713e4155993a3dea90c7437a
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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